class_docstr: Final output data type of agent connectors.
  
  Args are populated depending on the AgentConnector settings.
  The branching happens in ViewRequirementAgentConnector.
  
  Args:
      raw_dict: The raw input dictionary that sampler can use to
          build episodes and training batches.
          This raw dict also gets passed into ActionConnectors in case
          it contains data useful for action adaptation (e.g. action masks).
      sample_batch: The SampleBatch that can be immediately used for
          querying the policy for next action.
code: def __init__(
      self,
      raw_dict: Dict[str, TensorStructType],
      sample_batch: "SampleBatch",
  ):
      self.raw_dict = raw_dict
      self.sample_batch = sample_batch
  
file_path: ray/rllib/utils/typing.py
imports: from typing import TYPE_CHECKING, Any, Callable, Dict, Hashable, List, Optional, Tuple, Type, TypeVar, Union
  import numpy
  import gymnasium
  from ray.rllib.utils.annotations import ExperimentalAPI
label_desc: Use image node_id 1 to create a new AgentConnectorsOutput object  with example: obj = AgentConnectorsOutput(raw_dict, sample_batch)